






'''simple docstring'''







import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
							from transformers import (
							    FlaxBertModel,
							    FlaxCLIPVisionModel,
							    FlaxVisionTextDualEncoderModel,
							    FlaxViTModel,
							    VisionTextDualEncoderConfig,
							    VisionTextDualEncoderProcessor,
							)
							from transformers.modeling_flax_pytorch_utils import (
							    convert_pytorch_state_dict_to_flax,
							    load_flax_weights_in_pytorch_model,
							)


if is_torch_available():
							import torch

							from transformers import VisionTextDualEncoderModel

if is_vision_available():
							from PIL import Image
def     A__      (					__lowerCAmelCase		:      Any    ):
						if isinstance(__lowerCAmelCase ,     collections.abc.Iterable    ):
												return x
						return (x, x)




@require_flax
class 		UpperCamelCase__     :




		'''simple docstring'''





		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								pass






		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	np.abs((a - b)							).max()
								self.assertLessEqual(_lowerCAmelCase ,_lowerCAmelCase ,F'''Difference between torch and flax is {diff} (>= {tol}).'''							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__								=	VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel(_lowerCAmelCase							)

								lowerCamelCase__								=	model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase							)

								self.assertEqual(output["""text_embeds"""].shape ,(input_ids.shape[0], config.projection_dim)							)
								self.assertEqual(output["""image_embeds"""].shape ,(pixel_values.shape[0], config.projection_dim)							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	{"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase							)

								lowerCamelCase__								=	model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase							)

								self.assertEqual(output["""text_embeds"""].shape ,(input_ids.shape[0], model.config.projection_dim)							)
								self.assertEqual(output["""image_embeds"""].shape ,(pixel_values.shape[0], model.config.projection_dim)							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	{"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase							)

								lowerCamelCase__								=	model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase							)
								lowerCamelCase__								=	output[0]

								with tempfile.TemporaryDirectory() as tmpdirname:
														model.save_pretrained(_lowerCAmelCase							)
														lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase							)

														lowerCamelCase__								=	model(input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase							)
														lowerCamelCase__								=	after_output[0]
														lowerCamelCase__								=	np.amax(np.abs(out_a - out_a							)							)
														self.assertLessEqual(_lowerCAmelCase ,1E-3							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_vision_text_model(_lowerCAmelCase ,_lowerCAmelCase							)
								lowerCamelCase__								=	{"""vision_model""": vision_model, """text_model""": text_model}
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCAmelCase							)

								lowerCamelCase__								=	model(
								    input_ids=_lowerCAmelCase ,pixel_values=_lowerCAmelCase ,attention_mask=_lowerCAmelCase ,output_attentions=_lowerCAmelCase							)

								lowerCamelCase__								=	output.vision_model_output.attentions
								self.assertEqual(len(_lowerCAmelCase							) ,vision_config.num_hidden_layers							)

								# in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
								lowerCamelCase__								=	to_atuple(vision_model.config.image_size							)
								lowerCamelCase__								=	to_atuple(vision_model.config.patch_size							)
								lowerCamelCase__								=	(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
								lowerCamelCase__								=	num_patches + 1
								self.assertEqual(vision_attentions[0].shape[-3:] ,(vision_config.num_attention_heads, seq_len, seq_len)							)

								lowerCamelCase__								=	output.text_model_output.attentions
								self.assertEqual(len(_lowerCAmelCase							) ,text_config.num_hidden_layers							)

								self.assertEqual(
								    text_attentions[0].shape[-3:] ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1]) ,)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								pt_model.to(_lowerCAmelCase							)
								pt_model.eval()

								# prepare inputs
								lowerCamelCase__								=	inputs_dict
								lowerCamelCase__								=	{k: torch.tensor(v.tolist()							) for k, v in flax_inputs.items()}

								with torch.no_grad():
														lowerCamelCase__								=	pt_model(**_lowerCAmelCase							).to_tuple()

								lowerCamelCase__								=	fx_model(**_lowerCAmelCase							).to_tuple()
								self.assertEqual(len(_lowerCAmelCase							) ,len(_lowerCAmelCase							) ,"""Output lengths differ between Flax and PyTorch"""							)
								for fx_output, pt_output in zip(fx_outputs[:4] ,pt_outputs[:4]							):
														self.assert_almost_equals(_lowerCAmelCase ,pt_output.numpy() ,4E-2							)

								# PT -> Flax
								with tempfile.TemporaryDirectory() as tmpdirname:
														pt_model.save_pretrained(_lowerCAmelCase							)
														lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_pt=_lowerCAmelCase							)

								lowerCamelCase__								=	fx_model_loaded(**_lowerCAmelCase							).to_tuple()
								self.assertEqual(len(_lowerCAmelCase							) ,len(_lowerCAmelCase							) ,"""Output lengths differ between Flax and PyTorch"""							)
								for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4] ,pt_outputs[:4]							):
														self.assert_almost_equals(_lowerCAmelCase ,pt_output.numpy() ,4E-2							)

								# Flax -> PT
								with tempfile.TemporaryDirectory() as tmpdirname:
														fx_model.save_pretrained(_lowerCAmelCase							)
														lowerCamelCase__								=	VisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase ,from_flax=_lowerCAmelCase							)

								pt_model_loaded.to(_lowerCAmelCase							)
								pt_model_loaded.eval()

								with torch.no_grad():
														lowerCamelCase__								=	pt_model_loaded(**_lowerCAmelCase							).to_tuple()

								self.assertEqual(len(_lowerCAmelCase							) ,len(_lowerCAmelCase							) ,"""Output lengths differ between Flax and PyTorch"""							)
								for fx_output, pt_output_loaded in zip(fx_outputs[:4] ,pt_outputs_loaded[:4]							):
														self.assert_almost_equals(_lowerCAmelCase ,pt_output_loaded.numpy() ,4E-2							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	VisionTextDualEncoderModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel(_lowerCAmelCase							)

								lowerCamelCase__								=	convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,_lowerCAmelCase							)
								lowerCamelCase__								=	fx_state

								self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCAmelCase ,_lowerCAmelCase							)

								lowerCamelCase__								=	VisionTextDualEncoderModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel(_lowerCAmelCase							)

								lowerCamelCase__								=	load_flax_weights_in_pytorch_model(_lowerCAmelCase ,fx_model.params							)

								self.check_pt_flax_equivalence(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_model_from_pretrained_configs(**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_vision_text_dual_encoder_from_pretrained(**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_save_load(**_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								self.check_vision_text_output_attention(**_lowerCAmelCase							)






		@is_pt_flax_cross_test
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.prepare_config_and_inputs()
								lowerCamelCase__								=	config_inputs_dict.pop("""vision_config"""							)
								lowerCamelCase__								=	config_inputs_dict.pop("""text_config"""							)

								lowerCamelCase__								=	config_inputs_dict

								self.check_equivalence_pt_to_flax(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)
								self.check_equivalence_flax_to_pt(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							)






		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__							,      lowerCamelCase__								=	self.get_pretrained_model_and_inputs()

								lowerCamelCase__								=	model_a(**_lowerCAmelCase							)
								lowerCamelCase__								=	outputs[0]

								with tempfile.TemporaryDirectory() as tmp_dirname:
														model_a.save_pretrained(_lowerCAmelCase							)
														lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCAmelCase							)

														lowerCamelCase__								=	model_a(**_lowerCAmelCase							)
														lowerCamelCase__								=	after_outputs[0]
														lowerCamelCase__								=	np.amax(np.abs(out_a - out_a							)							)
														self.assertLessEqual(_lowerCAmelCase ,1E-5							)




@require_flax
class 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-vit""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
								lowerCamelCase__								=	13
								lowerCamelCase__								=	floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]							)
								lowerCamelCase__								=	ids_tensor([batch_size, 4] ,model.config.text_config.vocab_size							)
								lowerCamelCase__								=	random_attention_mask([batch_size, 4]							)
								lowerCamelCase__								=	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

								return model, inputs






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	FlaxViTModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxBertModel(_lowerCAmelCase							)
								return vision_model, text_model






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxViTModelTester(self							)
								lowerCamelCase__								=	FlaxBertModelTester(self							)
								lowerCamelCase__								=	vit_model_tester.prepare_config_and_inputs()
								lowerCamelCase__								=	bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase__							,      lowerCamelCase__								=	vision_config_and_inputs

								lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}




@require_torch
class 		UpperCamelCase__     (a ,unittest.TestCase      ):




		'''simple docstring'''





		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
								    """hf-internal-testing/tiny-random-clip""" ,"""hf-internal-testing/tiny-bert""" ,vision_from_pt=_lowerCAmelCase ,text_from_pt=_lowerCAmelCase ,)
								lowerCamelCase__								=	13
								lowerCamelCase__								=	floats_tensor(
								    [
								        batch_size,
								        model.config.vision_config.num_channels,
								        model.config.vision_config.image_size,
								        model.config.vision_config.image_size,
								    ]							)
								lowerCamelCase__								=	ids_tensor([batch_size, 4] ,model.config.text_config.vocab_size							)
								lowerCamelCase__								=	random_attention_mask([batch_size, 4]							)
								lowerCamelCase__								=	{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

								return model, inputs






		def       UpperCamelCase_       (   self ,_lowerCAmelCase ,_lowerCAmelCase							):
								lowerCamelCase__								=	FlaxCLIPVisionModel(_lowerCAmelCase							)
								lowerCamelCase__								=	FlaxBertModel(_lowerCAmelCase							)
								return vision_model, text_model






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxCLIPVisionModelTester(self							)
								lowerCamelCase__								=	FlaxBertModelTester(self							)
								lowerCamelCase__								=	clip_model_tester.prepare_config_and_inputs()
								lowerCamelCase__								=	bert_model_tester.prepare_config_and_inputs()

								lowerCamelCase__							,      lowerCamelCase__								=	vision_config_and_inputs

								lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__							,      lowerCamelCase__								=	text_config_and_inputs

								# make sure that cross attention layers are added
								return {
								    "text_config": text_config,
								    "vision_config": vision_config,
								    "pixel_values": pixel_values,
								    "attention_mask": attention_mask,
								    "input_ids": input_ids,
								    "token_type_ids": token_type_ids,
								}




@require_flax
@require_vision
class 		UpperCamelCase__     (unittest.TestCase      ):




		'''simple docstring'''





		@slow
		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian""" ,logit_scale_init_value=1.0							)
								lowerCamelCase__								=	VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""							)

								lowerCamelCase__								=	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""							)
								lowerCamelCase__								=	processor(
								    text=["""una foto di un gatto""", """una foto di un cane"""] ,images=_lowerCAmelCase ,padding=_lowerCAmelCase ,return_tensors="""np"""							)

								lowerCamelCase__								=	model(**_lowerCAmelCase							)

								# verify the logits
								self.assertEqual(outputs.logits_per_image.shape ,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])							)
								self.assertEqual(
								    outputs.logits_per_text.shape ,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]) ,)

								lowerCamelCase__								=	np.array([[1.228_4727, 0.310_4122]]							)

								self.assertTrue(np.allclose(outputs.logits_per_image ,_lowerCAmelCase ,atol=1E-3							)							)






'''simple docstring'''



import unittest

import numpy as np

from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import DonutImageProcessor
class 						lowerCamelCase_				(       unittest.TestCase      ):







						def __init__(	self :   Dict					,     lowerCAmelCase__ :   Union[str, Any]					,     lowerCAmelCase__ :   Dict=7					,     lowerCAmelCase__ :   List[str]=3					,     lowerCAmelCase__ :   Union[str, Any]=18					,     lowerCAmelCase__ :   Tuple=30					,     lowerCAmelCase__ :   Any=4_00					,     lowerCAmelCase__ :   Optional[int]=True					,     lowerCAmelCase__ :   List[Any]=None					,     lowerCAmelCase__ :   int=True					,     lowerCAmelCase__ :   Optional[Any]=False					,     lowerCAmelCase__ :   Any=True					,     lowerCAmelCase__ :   Optional[Any]=True					,     lowerCAmelCase__ :   Union[str, Any]=[0.5, 0.5, 0.5]					,     lowerCAmelCase__ :   List[str]=[0.5, 0.5, 0.5]					,     ):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      parent
													SCREAMING_SNAKE_CASE       :   int         =      batch_size
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      num_channels
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      image_size
													SCREAMING_SNAKE_CASE       :   Any         =      min_resolution
													SCREAMING_SNAKE_CASE       :   int         =      max_resolution
													SCREAMING_SNAKE_CASE       :   Tuple         =      do_resize
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      size if size is not None else {'''height''': 18, '''width''': 20}
													SCREAMING_SNAKE_CASE       :   List[Any]         =      do_thumbnail
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      do_align_axis
													SCREAMING_SNAKE_CASE       :   Tuple         =      do_pad
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      do_normalize
													SCREAMING_SNAKE_CASE       :   Union[str, Any]         =      image_mean
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      image_std







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_thumbnail": self.do_thumbnail,
													    "do_align_long_axis": self.do_align_axis,
													    "do_pad": self.do_pad,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													}


@require_torch
@require_vision
class 						lowerCamelCase_				(       snake_case_     ,					unittest.TestCase      ):
						_lowerCAmelCase  : Any						  =					DonutImageProcessor if is_vision_available() else None







						def        __lowercase     (	self :   str					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Tuple         =      DonutImageProcessingTester(self					)







						@property
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													return self.image_processor_tester.prepare_image_processor_dict()







						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   str         =      self.image_processing_class(**self.image_processor_dict					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''do_resize'''					)					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''size'''					)					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''do_thumbnail'''					)					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''do_align_long_axis'''					)					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''do_pad'''					)					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''do_normalize'''					)					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''image_mean'''					)					)
													self.assertTrue(hasattr(lowerCAmelCase__					,     '''image_std'''					)					)







						def        __lowercase     (	self :   Any					):

													"""simple docstring"""
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.image_processing_class.from_dict(self.image_processor_dict					)
													self.assertEqual(image_processor.size					,     {'''height''': 18, '''width''': 20}					)

													SCREAMING_SNAKE_CASE       :   List[str]         =      self.image_processing_class.from_dict(self.image_processor_dict					,     size=42					)
													self.assertEqual(image_processor.size					,     {'''height''': 42, '''width''': 42}					)

													# Previous config had dimensions in (width, height) order
													SCREAMING_SNAKE_CASE       :   Dict         =      self.image_processing_class.from_dict(self.image_processor_dict					,     size=(42, 84)					)
													self.assertEqual(image_processor.size					,     {'''height''': 84, '''width''': 42}					)







						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													pass







						@is_flaky()
						def        __lowercase     (	self :   int					):

													"""simple docstring"""
													# Initialize image_processing
													SCREAMING_SNAKE_CASE       :   Optional[int]         =      self.image_processing_class(**self.image_processor_dict					)
													# create random PIL images
													SCREAMING_SNAKE_CASE       :   List[str]         =      prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase__					)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__					,     Image.Image					)

													# Test not batched input
													SCREAMING_SNAKE_CASE       :   Any         =      image_processing(image_inputs[0]					,     return_tensors='''pt'''					).pixel_values
													self.assertEqual(
													    encoded_images.shape					,     (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )					,     )

													# Test batched
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      image_processing(lowerCAmelCase__					,     return_tensors='''pt'''					).pixel_values
													self.assertEqual(
													    encoded_images.shape					,     (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )					,     )







						@is_flaky()
						def        __lowercase     (	self :   Union[str, Any]					):

													"""simple docstring"""
													# Initialize image_processing
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.image_processing_class(**self.image_processor_dict					)
													# create random numpy tensors
													SCREAMING_SNAKE_CASE       :   Tuple         =      prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase__					,     numpify=lowerCAmelCase__					)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__					,     np.ndarray					)

													# Test not batched input
													SCREAMING_SNAKE_CASE       :   int         =      image_processing(image_inputs[0]					,     return_tensors='''pt'''					).pixel_values
													self.assertEqual(
													    encoded_images.shape					,     (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )					,     )

													# Test batched
													SCREAMING_SNAKE_CASE       :   int         =      image_processing(lowerCAmelCase__					,     return_tensors='''pt'''					).pixel_values
													self.assertEqual(
													    encoded_images.shape					,     (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )					,     )







						@is_flaky()
						def        __lowercase     (	self :   List[Any]					):

													"""simple docstring"""
													# Initialize image_processing
													SCREAMING_SNAKE_CASE       :   Optional[Any]         =      self.image_processing_class(**self.image_processor_dict					)
													# create random PyTorch tensors
													SCREAMING_SNAKE_CASE       :   Tuple         =      prepare_image_inputs(self.image_processor_tester					,     equal_resolution=lowerCAmelCase__					,     torchify=lowerCAmelCase__					)
													for image in image_inputs:
																				self.assertIsInstance(lowerCAmelCase__					,     torch.Tensor					)

													# Test not batched input
													SCREAMING_SNAKE_CASE       :   str         =      image_processing(image_inputs[0]					,     return_tensors='''pt'''					).pixel_values
													self.assertEqual(
													    encoded_images.shape					,     (
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )					,     )

													# Test batched
													SCREAMING_SNAKE_CASE       :   List[str]         =      image_processing(lowerCAmelCase__					,     return_tensors='''pt'''					).pixel_values
													self.assertEqual(
													    encoded_images.shape					,     (
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.size['''height'''],
													        self.image_processor_tester.size['''width'''],
													    )					,     )




